Principal Data Scientist - Bayesian

Job Title: Principal Data Scientist

Location: Fully Remote

Compensation: $225,000 – $250,000 base, bonus, + equity

Role: Principal Data Scientist (Bayesian Forecasting)

A rapidly growing B2B SaaS company is reimagining how businesses forecast demand and optimize pricing. With a recent investment in infrastructure and ML, the company is building a new forecasting engine from the ground up, centered around scalable, hierarchical Bayesian models that can operate across thousands of entities, each with highly variable, sparse data.

This role offers full ownership over the architecture and evolution of this system.

Why This Role Matters

This isn’t a feature team or a back-office analytics role. Forecasting is at the heart of the product, and accurate predictions directly impact customer revenue. You’ll lead the design and deployment of models that help real-world businesses make better pricing decisions in uncertain, highly seasonal environments.

The modeling challenges are complex, the data is messy, and the business impact is high. You'll have a greenfield mandate and the support of a strong technical team.

What You’ll Do

• Build and deploy hierarchical Bayesian time series models across thousands of data streams
• Translate sparse and irregular behavioral data into robust probabilistic forecasts
• Own the forecasting engine and experimentation platform end to end
• Design and prototype anomaly detection and multi-entity/group-level forecasting systems
• Collaborate with engineering, ML, and product teams to deploy models into production
• Operate within a modern MLOps stack to ensure scalable, repeatable experimentation and delivery

Tech Stack

Languages/Frameworks: Python, PyMC3, TensorFlow, PyTorch, scikit-learn

Infra: AWS SageMaker, MLflow

Modeling: Hierarchical Bayesian models, probabilistic programming, time series forecasting

Deployment: Batch and real-time ML pipelines powering external APIs

Ideal Candidate

• Ample hands-on experience in data science or applied ML
• Deep hands-on expertise in Bayesian modeling, time series, and probabilistic methods
• Experience with forecasting in sparse, multi-entity environments
• Comfortable designing and owning production-grade ML systems
• Thrives in ambiguous, greenfield settings and enjoys solving hard technical problems with business impact
• Bonus: Experience in pricing, optimization, or real-world forecasting use cases

About Us

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